The oil and gas industry attempts to maximize profitability in a dynamic and uncertain environment while satisfying a variety of constraints. Practitioners have attempted to improve oilfield operations by using better technology and appropriate business processes, among other things. Current practices of production optimization often involve combining mathematical models, field data and experience to make decisions about optimal production scenarios. Often, mid-term decisions are made by performing multiple future production scenario forecasts and selecting the best scenario. However, the selected scenario may not be followed in practice due to various inevitable practical difficulties. As a result, it is required to feedback the deviations from the plan and dynamically reoptimize under the most current production conditions. But updating the numerical reservoir model with new field data through history matching is a laborious task. It is further made difficult by the increasing real time measurements available today that increase the frequency at which field data can be collected. In addition, updating is seriously limited by the discontinuities in the models used by reservoir and production engineers to address the holistic production optimization of the entire field at all time scales. With increasing emphasis on risk analysis that requires several runs of large numerical models, it is imperative to use alternative methods.
Traditional approaches to production optimization workflows often make simplifying assumptions and work within artificial boundaries, to lower the complexity of an all-encompassing optimization problem. While this decomposition creates manageable workflows, it does not adequately support the integration of production optimization at multiple levels.
A number of proxy modeling techniques have been proposed where the output variables (oil recovery factor, multi-phase flow rates etc.) are modeled as a function of the input parameters selected through design of experiments (DOE). However, most of these methods focus on data-driven approaches such as response surface techniques based on regression, interpolation, neural network, etc. These methods are relatively easy to setup and capture the nonlinear effects in the training data set. However, reservoir phenomena unseen in the past (e.g., water breakthrough) or operating regimes that lie outside the range of training data set are not adequately predicted by such models. Further, most proxy modeling approaches used in production optimization actually model the reservoir simulator outputs and are seldom validated against real field data. Therefore, there is a need for an integrated model combining the reservoir and production engineering domains.
Additionally, the use of a collaborative environment adds considerable value to the operation of oil and gas assets. The value achieved is maximized when asset personnel can access the right information in an easy, fast and comprehensive manner. In this respect, assets that invest significantly on measurement and automation demand technologies that allow the users to capture, validate and make use of data in business workflows on a real-time basis.
Integrated production operations require coordination of every sector involved to impact the final performance of the asset in the most efficient way. Field personnel often have to perform complex tasks ranging from acquiring field measurements under the best known conditions of the reservoir and plant, analysis and validation of data collected, updating well and field models, and making timely decisions in accordance with asset studies and annual plans.
The implementation of real time operations (RTO) technologies for producing fields enables asset teams to effectively execute workflows related to well production testing, production test validation, production estimation, production losses control plant efficiency and key performance indicators management. The adopted workflows are enabled through appropriate change management processes in addition to innovative technologies. Reliable and time-effective workflows for production surveillance and testing, continuous performance modeling, and sharing consistent and validated data across multi-disciplinary teams provides better control of operations for the asset management.
Value opportunities exist for these asset operations. Among others, there are at least three clear areas of need which touch across most of the asset performance work processes, including:
Visualization: A coherent strategy to monitor the operations of the asset by providing access to the right data, and standardized rules to convert data into information by involving key people to interpret the information and transform it into knowledge;
Modeling: Make use of Real Time data to continuously optimize operations by validating the models of wells, reservoirs and operations; and
Automation: Direct control over the operational variables and platform actuators in an automated and closed loop with the previous two efforts, in order to effectively make decisions that have been already conditioned and validated by the asset managers in different scenarios.
Thus, there is a need for a methodology to select relevant technologies and a phased approach to implement the different workflows.